Hi everyone, I am trying to create simulations with perfect foresight by giving initial values to the state variables in histval. Unfortunately when I provide values to some state variable either I get homotopy issue or unreasonable dynamics. the model_info command shows the state variables, some of which are price levels (P_NTSPI for example) and relative prices (RP_flex), I wonder if I should give every state variable an initial value or not? the model is heavy and i don’t want to complicate it more. Here are the state variables:
PIE_FLEX(-1)
PIE4_NTSPI(-1)
PIE_PRT(-1)
P_NTSPI(-1)
P(-1)
P_REG(-1)
P_FLEX(-1)
P_food_imp(-1)
P_oil_imp(-1)
P_PRT(-1)
P_F(-1)
RP_REG_t(-1)
gRP_REG_t(-1)
RP_REG_g(-1)
RP_FLEX_t(-1)
gRP_FLEX_t(-1)
RP_FLEX_g(-1)
qoil_t(-1)
d_qoil_t(-1)
qoil_g(-1)
qfood_t(-1)
d_qfood_t(-1)
qfood_g(-1)
EPS_PIE_NTSPI(-1)
P_HEADLINE(-1)
C(-1)
EPS_PIE_NTSPI_EXP(-1)
EPS_C(-1)
PIE_STAR(-1)
Y(-1)
EPS_Y(-1)
CUMSUMY(-1)
DL_GDP_BAR(-1)
G_GDP_BAR(-1)
UNR_GAP(-1)
G_UNR(-1)
EPS_UNR_BAR(-1)
RS(-1)
TERM6M(-1)
TERM1Y(-1)
TERM2Y(-1)
TERM3Y(-1)
TERM5Y(-1)
TERM10Y(-1)
RR_BAR(-1)
EFF_RR_BAR(-1)
sigma(-1)
EPS_RS(-1)
P_GAP(-1)
PREM(-1)
RR_US(-1)
RS_US(-1)
PREM_BAR(-1)
FX_PREM(-1)
PIE_US(-1)
P_US(-1)
Z(-1)
Z_t(-1)
dZ_t(-1)
Z_US(-1)
Z_US_g(-1)
Z_US_t(-1)
Z_PRT(-1)
Z_PRT_g(-1)
Z_PRT_t(-1)
dZ_PRT_t(-1)
S(-1)
S_PRT(-1)
P_NTSPI(-2) (original expression P_NTSPI(-1))
P_NTSPI(-4) (original expression AUX_ENDO_LAG_14_1(-1))
P_NTSPI(-6) (original expression AUX_ENDO_LAG_14_2(-1))
P_FLEX(-2) (original expression P_FLEX(-1))
P_FLEX(-4) (original expression AUX_ENDO_LAG_17_1(-1))
P_FLEX(-6) (original expression AUX_ENDO_LAG_17_2(-1))
P(-2) (original expression P(-1))
P(-4) (original expression AUX_ENDO_LAG_15_1(-1))
P(-6) (original expression AUX_ENDO_LAG_15_2(-1))
P_REG(-2) (original expression P_REG(-1))
P_REG(-4) (original expression AUX_ENDO_LAG_16_1(-1))
P_REG(-6) (original expression AUX_ENDO_LAG_16_2(-1))
Y(-2) (original expression Y(-1))
Y(-4) (original expression AUX_ENDO_LAG_53_1(-1))
Y(-6) (original expression AUX_ENDO_LAG_53_2(-1))
P_US(-2) (original expression P_US(-1))
P_US(-4) (original expression AUX_ENDO_LAG_109_1(-1))
P_US(-6) (original expression AUX_ENDO_LAG_109_2(-1))
I also attach the model.
Thank you!
endo_nonlinear_loss_LR_est_final1.mod (63.2 KB)